Podcast: Context Engineering with Adi Polak
This matters because enterprise architecture decisions around AI, data, and platform engineering define long-term competitiveness and operational efficiency.
Podcast: Context Engineering with Adi Polak
In this episode, Thomas Betts and Adi Polak talk about the need for context engineering when interacting with LLMs and designing agentic systems. Prompt engineering techniques work with a stateless approach, while con...
Editorial Analysis
Context engineering represents a fundamental shift in how we architect data pipelines for AI systems. While prompt engineering treats LLMs as stateless functions, real enterprise systems need persistent, evolving context layers that track conversation history, entity relationships, and domain-specific knowledge. For data engineers, this means building robust context stores—think vector databases, knowledge graphs, or time-series event streams—that feed agentic systems with accurate, relevant information. The architectural implication is clear: your modern data stack now needs a dedicated context management layer sitting between your operational data and your AI applications. We're moving from one-shot inference to stateful agent interactions, which demands proper data governance, freshness guarantees, and lineage tracking. The teams winning here are treating context engineering as a first-class data engineering problem, not an afterthought in ML operations.